Reproducing the figures of the manuscript

Introduction

This markdown document reproduces the figures of the manuscript: Multi-omic characterization of human sural nerves across polyneuropathies. All relevant data are automatically downloaded from Zenodo.

Instructions on how to restore the environment using renv or Docker can be found in the corresponding GitHub repository. The corresponding website, which contains interactive visualizations of the single nucleus transcriptomics and spatial transcriptomics data, is available at pns-atlas.mzhlab.com. If you have any questions, please contact us at mheming.de.

Load libraries

library(qs)
library(Seurat)
library(tidyverse)
library(miloDE)
library(sessioninfo)
library(EnhancedVolcano)
library(pals)
library(scMisc)

Figure 1

UMAP of all cells (Figure 1B)

options(timeout = 3600)
if (!file.exists("umap_figure.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/umap_figure.qs?download=1",
        "umap_figure.qs"
    )
}
umap_figure <- qread("umap_figure.qs")
DimPlot(
    umap_figure,
    reduction = "umap.scvi.full",
    pt.size = .1,
    alpha = .1,
    cols = umap_figure@misc$cluster_col,
    label = TRUE,
    raster = FALSE
) +
    NoLegend() +
    theme(
        axis.text = element_blank(),
        axis.ticks = element_blank()
    ) +
    xlab("UMAP_1") +
    ylab("UMAP_2")

Figure 2

UMAP immune cells (Figure 2A)

ic_figure <- qread("ic_figure.qs")
DimPlot(
    ic_figure,
    reduction = "umap.rpca",
    pt.size = .1,
    alpha = .3,
    cols = ic_figure@misc$ic_cluster_col,
    label = TRUE,
    raster = FALSE
) +
    theme(
        axis.text = element_blank(),
        axis.ticks = element_blank()
    ) +
    NoLegend() +
    xlab("UMAP1") +
    ylab("UMAP2")

Feature plots immune cells (Figure 2B)

FeaturePlot(
    ic_figure,
    features = c("MS4A7", "CX3CR1", "TREM2", "LYVE1", "FOLR2", "TIMD4"),
    reduction = "umap.rpca",
    pt.size = 0.1,
    raster = FALSE,
    coord.fixed = TRUE,
    cols = c("#F0F0F0", "#CB181D"),
    order = TRUE,
    ncol = 3
) &
    theme(
        axis.text = element_blank(),
        axis.ticks = element_blank()
    ) &
    xlab("UMAP1") &
    ylab("UMAP2")

Enrichment of gene ontology terms in markers genes of Macro 18 (Figure 2B)

if (!file.exists("enrichr_macro18.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/enrichr_macro18.qs?download=1",
        "enrichr_macro18.qs"
    )
}
enrichr_macro18 <- qread("enrichr_macro18.qs")
enrichr_macro18 |>
    dplyr::slice_min(order_by = Adjusted.P.value, n = 10, with_ties = FALSE) |>
    tidyr::separate(Overlap, into = c("overlap1", "overlap2")) |> # separate overlap in two columns
    dplyr::mutate(
        Term = gsub(x = Term, pattern = "\\s\\(.+\\)", replacement = "")
    ) |>
    dplyr::mutate(overlap = as.numeric(overlap1) / as.numeric(overlap2)) |> # calculcate overlap
    ggplot(aes(
        y = reorder(Term, -log10(Adjusted.P.value)),
        x = -log10(Adjusted.P.value)
    )) +
    geom_col(fill = scales::hue_pal()(5)[1]) +
    labs(
        x = "-Log10 Adjusted P value",
        y = ""
    ) +
    theme_classic() +
    theme(legend.position = "none")

Dotplot of SAMC markers in IC (Figure 2D)

if (!file.exists("dotplot_data_ic_samc.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/dotplot_data_ic_samc.qs?download=1",
        "dotplot_data_ic_samc.qs"
    )
}

# Seurat DotPlot function (only ggplot part to avoid calculations)
DotPlotModified <- function(
    data.plot,
    cols = c("lightgrey", "blue"),
    dot.scale = 6,
    scale.by = "radius",
    scale.min = NA,
    scale.max = NA) {
    scale.func <- switch(
        EXPR = scale.by,
        "size" = scale_size,
        "radius" = scale_radius,
        stop("'scale.by' must be either 'size' or 'radius'")
    )
    color.by <- "avg.exp.scaled"
    plot <- ggplot(
        data = data.plot,
        mapping = aes_string(x = "features.plot", y = "id")
    ) +
        geom_point(mapping = aes_string(size = "pct.exp", color = color.by)) +
        scale.func(range = c(0, dot.scale), limits = c(scale.min, scale.max)) +
        theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
        guides(size = guide_legend(title = "Percent Expressed")) +
        cowplot::theme_cowplot()
    plot <- plot + guides(color = guide_colorbar(title = "Average Expression"))
    return(plot)
}

dotplot_data_ic_samc <- qread("dotplot_data_ic_samc.qs")

DotPlotModified(
    data.plot = dotplot_data_ic_samc,
    scale.by = "size"
) +
    viridis::scale_color_viridis(option = "viridis") +
    theme(
        axis.text.x = element_text(
            angle = 90,
            vjust = 0.5,
            hjust = 1,
            face = "italic"
        ),
    ) +
    xlab("") +
    ylab("")

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.

Warning: Removed 17 rows containing missing values or values outside the scale range
(`geom_point()`).

Feature plots of SAMC markers in IC (Figure 2E)

if (!file.exists("ic_figure.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/ic_figure.qs?download=1",
        "ic_figure.qs"
    )
}
ic_figure <- qread("ic_figure.qs")
plot_predicted_samc <- function(seu_obj) {
    # Create a data frame with alpha values, color values, and UMAP coordinates
    umap_coords <- Embeddings(seu_obj, "umap.rpca")
    alpha_values <- ifelse(seu_obj$stroke_label == "SAMC", 0.5, 0.001)
    color_values <- ifelse(seu_obj$stroke_label == "SAMC", "SAMC", "Other")
    color_map <- setNames(c("red", "grey"), c("SAMC", "Other"))
    plot_df <- data.frame(
        cell = rownames(umap_coords),
        UMAP1 = umap_coords[, 1],
        UMAP2 = umap_coords[, 2],
        alpha = alpha_values,
        color = color_values
    )

    # Create custom ggplot
    samc_predicted <- ggplot(
        plot_df,
        aes(x = UMAP1, y = UMAP2, alpha = alpha, color = color)
    ) +
        geom_point(size = 0.1) +
        scale_color_manual(values = color_map) + # Simplified color scale
        theme_minimal() +
        theme(
            axis.text = element_blank(),
            axis.ticks = element_blank(),
            panel.grid = element_blank(),
            panel.border = element_rect(color = "black", fill = NA, size = 1.0),
            panel.background = element_rect(fill = "white", color = NA),
            plot.background = element_rect(fill = "white", color = NA),
            legend.position = "none"
        ) +
        ggtitle("predicted SAMC")
    return(samc_predicted)
}

plot_predicted_samc(ic_figure)

Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.

Figure 3

Propeller abundance PNP vs CTRL (Figure 3A)

if (!file.exists("propeller_PNP_CTRL.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/propeller_PNP_CTRL.qs?download=1",
        "propeller_PNP_CTRL.qs"
    )
}
if (!file.exists("propeller_PNP_CTRL_ic.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/propeller_PNP_CTRL_ic.qs?download=1",
        "propeller_PNP_CTRL_ic.qs"
    )
}
propeller_PNP_CTRL <- qread("propeller_PNP_CTRL.qs")
propeller_PNP_CTRL_ic <- qread("propeller_PNP_CTRL_ic.qs")

# function to plot custom dotplot
dotplotPropeller <- function(data, color, title) {
    ggplot(
        data,
        aes(x = log2ratio, y = fct_reorder(cluster, log2ratio), color = cluster)
    ) +
        geom_point(size = 5) +
        theme_classic() +
        geom_vline(
            xintercept = 0,
            color = "red",
            linetype = "solid"
        ) +
        scale_color_manual(values = color) +
        xlab("Log2 fold change") +
        ylab(NULL) +
        theme(legend.position = "none") +
        ggtitle(title)
}
dotplotPropeller(
    propeller_PNP_CTRL,
    color = umap_figure@misc$cluster_col,
    title = "PNP vs CTRL main clusters"
)

dotplotPropeller(
    propeller_PNP_CTRL_ic,
    color = ic_figure@misc$ic_cluster_col,
    title = "PNP vs CTRL immune cells"
)

Covarying Neighborhood Analysis (Figure 3B)

if (!file.exists("cna_pnp_gratio_figure.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/cna_pnp_gratio_figure.qs?download=1",
        "cna_pnp_gratio_figure.qs"
    )
}
if (!file.exists("cna_incat_figure.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/cna_incat_figure.qs?download=1",
        "cna_incat_figure.qs"
    )
}
cna_pnp_gratio_figure <- qread("cna_pnp_gratio_figure.qs")
cna_incat_figure <- qread("cna_incat_figure.qs")

# function to plot CNA feature plot
fplotCNA <- function(object, feature, title) {
    FeaturePlot(
        object,
        reduction = "umap.scvi.full",
        features = feature,
        pt.size = 0.1,
        order = FALSE,
        coord.fixed = TRUE,
        raster = FALSE,
        alpha = 0.2
    ) +
        scale_colour_gradient2(
            low = "#2166AC",
            mid = "white",
            high = "#B2182B",
            midpoint = 0,
        ) +
        theme(
            axis.text = element_blank(),
            axis.ticks = element_blank(),
        ) +
        labs(
            title = title,
            color = "Correlation",
            x = "UMAP1",
            y = "UMAP2"
        )
}

fplotCNA(cna_pnp_gratio_figure, "cna_ncorrs_pnp", title = "PNP")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

fplotCNA(cna_incat_figure, "cna_ncorrs", title = "INCAT")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

fplotCNA(cna_pnp_gratio_figure, "cna_ncorrs_gratio", title = "g-ratio")

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Number of DEGs (Figure 3C)

if (!file.exists("pnp_ctrl_pseudobulk_de.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/pnp_ctrl_pseudobulk_de.qs?download=1",
        "pnp_ctrl_pseudobulk_de.qs"
    )
}
pnp_ctrl_pseudobulk <- qread("pnp_ctrl_pseudobulk_de.qs")
pnp_ctrl_pseudobulk |>
    mutate(cluster = fct_reorder(cluster, n)) |>
    ggplot(aes(x = cluster, y = n, fill = cluster)) +
    geom_col() +
    coord_flip() +
    scale_fill_manual(values = umap_figure@misc$cluster_col) +
    theme_classic() +
    theme(legend.position = "none") +
    labs(
        x = "",
        y = "",
        title = "PNP vs CTRL"
    )

MiloDE DEGs PNP vs CTRL (Figure 3D)

if (!file.exists("milo_figure.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/milo_figure.qs?download=1",
        "milo_figure.qs"
    )
}

milo_figure <- qread("milo_figure.qs")
plotMiloDE <- function(condition, title) {
    plot <-
        plot_milo_by_single_metric(
            milo_figure$obj,
            milo_figure$stat[[condition]],
            colour_by = "n_DE_genes",
            layout = "UMAP.SCVI.FULL",
            size_range = c(0.5, 5),
            edge_width = c(0.1, 1.0),
            edge_weight.thres = 10
        ) +
        viridis::scale_fill_viridis(name = "# DE genes", option = "inferno") +
        ggtitle(title)
    print(plot)
}

plotMiloDE("pnp", "PNP vs CTRL")

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

Pseudobulk DE PNP vs CTRL (Figure 3E)

if (!file.exists("de_pseudo_pnp_ctrl.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/de_pseudo_pnp_ctrl.qs?download=1",
        "de_pseudo_pnp_ctrl.qs"
    )
}
de_pseudo_pnp_ctrl <- qread("de_pseudo_pnp_ctrl.qs")

# volcano plot function
volcanoPlot <- function(
    cluster,
    input,
    FCcutoff = 2,
    selectLab = NULL,
    drawConnectors = TRUE,
    condition1,
    condition2) {
    input <- input[[cluster]]
    volcano <- EnhancedVolcano::EnhancedVolcano(
        data.frame(input),
        lab = paste0("italic('", input[["gene"]], "')"),
        x = "avg_logFC",
        y = "p_val_adj",
        xlim = c(min(input[["avg_logFC"]], max(input[["avg_logFC"]]))),
        ylim = c(0, max(-log10(input[["p_val_adj"]]))),
        pCutoff = 0.1,
        FCcutoff = FCcutoff,
        axisLabSize = 15,
        pointSize = 2,
        labSize = 5,
        subtitle = NULL,
        caption = NULL,
        border = "full",
        gridlines.major = FALSE,
        gridlines.minor = FALSE,
        drawConnectors = drawConnectors,
        lengthConnectors = unit(0.0001, "npc"),
        title = paste(condition1, "vs", condition2, "in ", cluster),
        boxedLabels = TRUE,
        selectLab = selectLab,
        xlab = bquote(~ Log[2] ~ "fold change"),
        ylab = bquote(~ -Log[10] ~ "adjusted p-value"),
        parseLabels = TRUE,
        legendLabels = c(
            "NS",
            "logFC",
            "p-val",
            "p-val + logFC"
        ),
        legendPosition = "right",
    )
}

# define clusters of interest
cluster_de <- c("mySC", "nmSC", "repairSC", "PC2")

# define genes of interests
lab_pnp_ctrl <- list(
    "mySC" = paste0(
        "italic('",
        c("DCN", "TNXB", "COL1A1", "COL15A1", "CD53", "IL4R", "CD74"),
        "')"
    ),
    "nmSC" = paste0(
        "italic('",
        c("IL10RA", "IL13RA1", "CSF2RA", "TGFBI"),
        "')"
    ),
    "repairSC" = paste0("italic('", c("GALR1", "TMEM47"), "')"),
    "PC2" = paste0(
        "italic('",
        c("MFAP5", "NLGN4Y", "PCDH11Y", "IFIT3", "OASL", "MX1"),
        "')"
    )
)

# plot volcano plots of selected clusters
purrr::walk(
    cluster_de,
    function(cluster) {
        print(volcanoPlot(
            cluster = cluster,
            input = de_pseudo_pnp_ctrl,
            FCcutoff = 2,
            condition1 = "PNP",
            condition2 = "CTRL",
            selectLab = lab_pnp_ctrl[[cluster]]
        ))
    }
)

Figure 4

Abundance of clusters per PNP subtype (Figure 4A)

if (!file.exists("abundance_main_clusters.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/abundance_main_clusters.qs?download=1",
        "abundance_main_clusters.qs"
    )
}
abundance_main_clusters <- qread("abundance_main_clusters.qs")

abundance_main_clusters |>
    ggplot() +
    geom_col(
        aes(x = type, y = count, fill = cell),
        color = "black",
        linewidth = 0.1,
        position = "fill"
    ) +
    scale_fill_manual(values = umap_figure@misc$cluster_col) +
    guides(fill = guide_legend(title = NULL)) +
    theme_classic() +
    ylab("Proportion of cells") +
    xlab("") +
    theme(
        axis.text.x = element_text(
            angle = 90,
            hjust = 1,
            vjust = 0.3
        )
    )

Propeller abundance of PNP subtypes main cluster (Figure 4B)

if (!file.exists("propeller_PNP_subtypes_main.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/propeller_PNP_subtypes_main.qs?download=1",
        "propeller_PNP_subtypes_main.qs"
    )
}

propeller_PNP_subtypes_main <- qread("propeller_PNP_subtypes_main.qs")
purrr::walk(
    c("VN", "CIDP", "CIAP"),
    function(condition) {
        print(dotplotPropeller(
            propeller_PNP_subtypes_main[[condition]],
            color = umap_figure@misc$cluster_col,
            title = paste0("CTRL vs ", condition, " main")
        ))
    }
)

Propeller abundance of PNP subtypes immune cluster (Figure 4C)

if (!file.exists("propeller_PNP_subtypes_ic.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/propeller_PNP_subtypes_ic.qs?download=1",
        "propeller_PNP_subtypes_ic.qs"
    )
}
propeller_PNP_subtypes_ic <- qread("propeller_PNP_subtypes_ic.qs")
purrr::walk(
    c("VN", "CIDP", "CIAP"),
    function(condition) {
        print(dotplotPropeller(
            propeller_PNP_subtypes_ic[[condition]],
            color = umap_figure@misc$cluster_col,
            title = paste0("CTRL vs ", condition, " immune")
        ))
    }
)

Manual PTPRC quantification in Xenium (Figure 4E)

if (!file.exists("manual_xenium_quantification.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/manual_xenium_quantification.qs?download=1",
        "manual_xenium_quantification.qs"
    )
}

manual_xenium_quantification <- qread("manual_xenium_quantification.qs")
plotQuanti <- function(y_value, title) {
    plot <-
        manual_xenium_quantification |>
        ggplot(aes(x = level2, y = .data[[y_value]], fill = level2)) +
        geom_boxplot() +
        geom_jitter(height = 0, width = 0.1) +
        scale_fill_manual(values = umap_figure@misc$level2_cols) +
        guides(fill = guide_legend(title = NULL)) +
        theme_classic() +
        xlab("") +
        ylab("") +
        ggtitle(title) +
        theme(
            legend.position = "none",
            axis.text.x = element_text(
                angle = 90,
                hjust = 1,
                vjust = 0.3,
            )
        )
    print(plot)
}

purrr::walk2(
    c("endoPTPRC_density_sum", "epiPTPRC_density"),
    c("PTPRC density endoneurial", "PTPRC density epineurial"),
    function(y_value, title) {
        plotQuanti(y_value = y_value, title = title)
    }
)

MiloDE DEGs PNP subtypes(Figure 4G)

purrr::walk2(
    c("vn", "cidp", "ciap"),
    c("VN vs CTRL", "CIDP vs CTRL", "CIAP vs CTRL"),
    function(condition, title) {
        plotMiloDE(condition, title)
    }
)

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.

Figure 5

CXCL14 expression in PNP (Figure 5A)

if (!file.exists("perineurial_figure.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/perineurial_figure.qs?download=1",
        "perineurial_figure.qs"
    )
}
perineurial_figure <- qread("perineurial_figure.qs")

VlnPlot(
    perineurial_figure,
    features = "CXCL14",
    group.by = "level2",
    cols = perineurial_figure@misc$level2_cols,
    pt.size = 0
) +
    NoLegend() +
    xlab("") +
    ylab("") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

Loading required package: BPCells

Warning: Converting to a dense matrix may use excessive memory
This message is displayed once every 8 hours.

Quantification of CXCL14 histololgy in the perineurium (Figure 5B)

if (!file.exists("cxcl14_data.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/cxcl14_data.qs?download=1",
        "cxcl14_data.qs"
    )
}
cxcl14_data <- qread("cxcl14_data.qs")
cxcl14_data$data |>
    ggplot(aes(
        x = sample,
        y = pct_positive,
        fill = diagnosis
    )) +
    geom_boxplot() +
    geom_jitter(width = 0.2, height = 0, size = 1) +
    theme_classic() +
    ylab("% positive CXCL14 in the perineurium") +
    scale_fill_manual(values = cxcl14_data$diagnosis_col)

Perineurium thickness CTRL vs CIDP (Figure 5E)

if (!file.exists("data_peri_diagnosis.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/data_peri_diagnosis.qs?download=1",
        "data_peri_diagnosis.qs"
    )
}
data_peri_diagnosis <- qread("data_peri_diagnosis.qs")

# function to plot perineurium area/perimeter ratios
plot_peri <- function(data, diagnosis_p_value, y_lab, diagnosis_col) {
    plot <- ggplot(
        data,
        aes(x = diagnosis, y = adjusted_value, fill = diagnosis)
    ) +
        geom_boxplot() +
        geom_jitter(width = 0.2, size = 1) +
        theme_classic() +
        ggsignif::geom_signif(
            comparisons = list(c("CTRL", "CIDP")),
            annotation = paste("p =", signif(diagnosis_p_value, 3))
        ) +
        scale_fill_manual(values = diagnosis_col) +
        theme(legend.position = "none") +
        xlab("") +
        ylab(paste0("Adjusted perineurium outer-inner ratio (", y_lab, ")"))
    return(plot)
}

lapply(
    data_peri_diagnosis,
    function(x) {
        plot_peri(
            data = x$data,
            diagnosis_p_value = x$p_value,
            y_lab = x$y_lab,
            diagnosis_col = x$diagnosis_col
        )
    }
)

[[1]]

[[2]]

Supplementary Figure 1

Demographics plot (Figure S1A)

if (!file.exists("demographics.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/demographics.qs?download=1",
        "demographics.qs"
    )
}

demographics <- qread("demographics.qs")

plotDemographics <- function(var) {
    plot <- demographics |>
        dplyr::filter(!is.na(.data[[var]])) |>
        ggplot(aes(x = level2, y = .data[[var]], fill = level2)) +
        geom_boxplot() +
        geom_point() +
        theme_bw() +
        scale_fill_manual(values = umap_figure@misc$level2_cols) +
        xlab("") +
        ylab("") +
        ggtitle(var) +
        theme(legend.position = "none")
    print(plot)
}

purrr::walk(c("age", "ncv_tibial_motoric", "incat"), plotDemographics)

demographics |>
    ggplot(aes(x = level2, fill = sex)) +
    geom_bar() +
    theme_bw() +
    scale_fill_manual(values = pals::cols25(2)) +
    xlab("") +
    ylab("") +
    ggtitle("sex")

UMAP grouped by center and sample (Figure S1C)

DimPlot(
    umap_figure,
    reduction = "umap.scvi.full",
    pt.size = .1,
    raster = FALSE,
    alpha = 0.1,
    group.by = "center",
    cols = pals::cols25(3),
    label = FALSE
) +
    theme(
        axis.text = element_blank(),
        axis.ticks = element_blank()
    ) +
    xlab("UMAP_1") +
    ylab("UMAP_2")

my_cols_50 <- unname(Polychrome::createPalette(50, pals::cols25()))

DimPlot(
    umap_figure,
    reduction = "umap.scvi.full",
    pt.size = .1,
    raster = FALSE,
    alpha = 0.1,
    group.by = "sample",
    cols = umap_figure@misc$sample_cols,
    label = FALSE
) +
    theme(
        axis.text = element_blank(),
        axis.ticks = element_blank()
    ) +
    xlab("UMAP_1") +
    ylab("UMAP_2")

Number of genes per nucleus (Figure S1D)

count_genes <- dplyr::tibble(
    feature = umap_figure@meta.data$nFeature_RNA,
    sample = umap_figure@meta.data$sample
)

count_genes |>
    ggplot(aes(x = sample, y = feature, fill = sample)) +
    geom_boxplot() +
    scale_fill_manual(values = umap_figure@misc$sample_cols) +
    theme_classic() +
    theme(legend.position = "none") +
    xlab("") +
    ylab("") +
    ggtitle("Number of genes per nucleus") +
    theme(axis.text.x = element_text(angle = 90))

Abundance of clusters per sample (Figure S1E)

if (!file.exists("abundance_main_clusters_sample.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/abundance_main_clusters_sample.qs?download=1",
        "abundance_main_clusters_sample.qs"
    )
}
abundance_main_clusters_sample <- qread("abundance_main_clusters_sample.qs")
abundance_main_clusters_sample |>
    mutate(type = factor(type, levels = rev(levels(factor(type))))) |>
    ggplot() +
    geom_col(
        aes(x = type, y = count, fill = cell),
        color = "black",
        linewidth = 0.1,
        position = "fill"
    ) +
    scale_fill_manual(values = umap_figure@misc$cluster_col) +
    theme_classic() +
    ylab("Proportion of cells") +
    xlab("") +
    theme(
        axis.text.x = element_text(
            angle = 90,
            hjust = 1,
            vjust = 0.3
        ),
        legend.position = "none"
    ) +
    coord_flip()

Supplementary Figure 2

Dotplot of main clusters(Figure S2A)

if (!file.exists("dotplot_data.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/dotplot_data.qs?download=1",
        "dotplot_data.qs"
    )
}
dotplot_data <- qread("dotplot_data.qs")


DotPlotModified(
    data.plot = dotplot_data,
    scale.by = "size"
) +
    viridis::scale_color_viridis(option = "viridis") +
    theme(
        axis.text.x = element_text(
            angle = 90,
            vjust = 0.5,
            hjust = 1,
            face = "italic",
            size = 7
        ),
        legend.position = "top",
        legend.text = element_text(size = 6),
        legend.title = element_text(size = 10)
    ) +
    xlab("") +
    ylab("")

Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.

Warning: Removed 688 rows containing missing values or values outside the scale range
(`geom_point()`).

Enrichment of gene ontology terms of markers genes in periC (Figure S2B)

if (!file.exists("enrichr_periC.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/enrichr_periC.qs?download=1",
        "enrichr_periC.qs"
    )
}
enrichr_periC <- qread("enrichr_periC.qs")

plotEnrichr <- function(data, name) {
    plot <-
        data |>
        dplyr::slice_min(
            order_by = Adjusted.P.value,
            n = 10,
            with_ties = FALSE
        ) |>
        tidyr::separate(Overlap, into = c("overlap1", "overlap2")) |>
        dplyr::mutate(
            Term = gsub(x = Term, pattern = "\\s\\(.+\\)", replacement = "")
        ) |>
        dplyr::mutate(overlap = as.numeric(overlap1) / as.numeric(overlap2)) |>
        ggplot(aes(
            y = reorder(Term, -log10(Adjusted.P.value)),
            x = -log10(Adjusted.P.value)
        )) +
        geom_col(fill = scales::hue_pal()(5)[1]) +
        labs(
            x = "-Log10 Adjusted P value",
            y = ""
        ) +
        theme_classic() +
        theme(legend.position = "none") +
        ggtitle(name)
    print(plot)
}

purrr::walk2(
    enrichr_periC,
    names(enrichr_periC),
    plotEnrichr
)

plotUMAP <- function(group, title) {
    plot <-
        DimPlot(
            umap_figure,
            reduction = "umap.scvi.full",
            group.by = group,
            raster = FALSE,
            pt.size = .1,
            alpha = .1,
            cols = umap_figure@misc$sample_cols,
            label = TRUE
        ) +
        theme(
            axis.text = element_blank(),
            axis.ticks = element_blank()
        ) +
        xlab("UMAP_1") +
        ylab("UMAP_2") +
        NoLegend() +
        ggtitle(title)
    print(plot)
}

purrr::walk2(
    c("milbrandt_sciatic_label_full", "suter_p60_label_full"),
    c("Yim et al. sciatic nerve", "Gerber et al. p60"),
    plotUMAP
)

if (!file.exists("ec_rosmap.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/ec_rosmap.qs?download=1",
        "ec_rosmap.qs"
    )
}
ec_rosmap <- qread("ec_rosmap.qs")
DimPlot(
    ec_rosmap,
    reduction = "umap.scvi.full",
    group.by = "rosmap_label",
    raster = FALSE,
    pt.size = .1,
    alpha = .1,
    cols = ec_rosmap@misc$sample_cols,
    label = TRUE
) +
    NoLegend() +
    xlim(-12, -5) +
    ylim(2, 10) +
    xlab("UMAP_1") +
    ylab("UMAP_2") +
    ggtitle("ROSMAP vascular cells") +
    theme(
        axis.text = element_blank(),
        axis.ticks = element_blank()
    )

Warning: Removed 53 rows containing missing values or values outside the scale range
(`geom_point()`).

Dotplot of new marker genes in rodent and human datasets (Figure S2D)

if (!file.exists("dotplot_human_rodent.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/dotplot_human_rodent.qs?download=1",
        "dotplot_human_rodent.qs"
    )
}
dotplot_human_rodent <- qread("dotplot_human_rodent.qs")

DotPlotCustom <-
    function(data, title) {
        plot <-
            DotPlotModified(
                data.plot = data,
                scale.by = "size",
                dot.scale = 10,
            ) +
            viridis::scale_color_viridis(option = "viridis") +
            theme(
                axis.text.x = element_text(
                    angle = 90,
                    vjust = 0.5,
                    hjust = 1,
                    face = "italic",
                )
            ) +
            xlab("") +
            ylab("") +
            ggtitle(title)
        print(plot)
    }

purrr::walk2(
    dotplot_human_rodent,
    c("rodent(Yim et al.)", "human (this study)"),
    DotPlotCustom
)

Warning: Removed 9 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 6 rows containing missing values or values outside the scale range
(`geom_point()`).

Supplementary Figure 4

Dotplot of immune cell marker genes (Figure S4A)

if (!file.exists("dotplot_data_ic.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/dotplot_data_ic.qs?download=1",
        "dotplot_data_ic.qs"
    )
}

dotplot_data_ic <- qread("dotplot_data_ic.qs")

DotPlotModified(
    data.plot = dotplot_data_ic,
    scale.by = "size"
) +
    viridis::scale_color_viridis(option = "viridis") +
    theme(
        axis.text.x = element_text(
            angle = 90,
            vjust = 0.5,
            hjust = 1,
            face = "italic",
            size = 7
        ),
        legend.position = "top",
        legend.text = element_text(size = 6),
        legend.title = element_text(size = 10)
    ) +
    xlab("") +
    ylab("")

Warning: Removed 967 rows containing missing values or values outside the scale range
(`geom_point()`).

Dot plot IGH chain genes (Supplementary Figure 4B)

if (!file.exists("dotplot_data_b_plasma.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/dotplot_data_b_plasma.qs?download=1",
        "dotplot_data_b_plasma.qs"
    )
}
dotplot_data_b_plasma <- qread("dotplot_data_b_plasma.qs")
DotPlotModified(
    data.plot = dotplot_data_b_plasma,
    scale.by = "size",
) +
    viridis::scale_color_viridis(option = "viridis") +
    theme(
        axis.text.x = element_text(
            angle = 90,
            vjust = 0.5,
            hjust = 1,
            face = "italic"
        ),
    ) +
    xlab("") +
    ylab("")

Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).

Enrichment of gene ontology terms in DEG (Figure S4C)

if (!file.exists("enrichr_de.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/enrichr_de.qs?download=1",
        "enrichr_de.qs"
    )
}

enrichr_de <- qread("enrichr_de.qs")

purrr::walk2(
    enrichr_de$pos,
    paste0(names(enrichr_de$pos), " upregulated in PNP"),
    plotEnrichr
)

purrr::walk2(
    enrichr_de$neg,
    paste0(names(enrichr_de$neg), " downregulated in PNP"),
    plotEnrichr
)

Supplementary Figure 5

Boxplots of g-ratio per sample (Figure S5B)

if (!file.exists("g_ratio.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/g_ratio.qs?download=1",
        "g_ratio.qs"
    )
}
g_ratio <- qread("g_ratio.qs")

g_ratio |>
    ggplot(aes(x = sample, y = g_ratio)) +
    geom_boxplot() +
    geom_jitter(height = 0, width = 0.1, alpha = 0.3, size = 0.1) +
    theme_bw() +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
    xlab("g-ratio") +
    ylab("")

Boxplots of g-ratio per PNP subtye (Figure S5C)

# function to plot boxplots Figure S5C-E
plotBoxplotCustom <-
    function(data, y_var) {
        data |>
            ggplot(aes(x = level2, y = .data[[y_var]], fill = level2)) +
            geom_boxplot() +
            geom_jitter(height = 0, width = 0.1) +
            scale_fill_manual(values = umap_figure@misc$level2_cols) +
            theme_classic() +
            ylab(y_var) +
            xlab("") +
            theme(
                axis.text.x = element_text(
                    angle = 90,
                    hjust = 1,
                    vjust = 0.3
                ),
                legend.position = "none"
            )
    }

g_ratio |>
    group_by(sample) |>
    mutate(g_ratio = mean(g_ratio)) |>
    select(sample, level2, g_ratio) |>
    distinct() |>
    plotBoxplotCustom(y_var = "g_ratio")

Boxplots of axon count per PNP subtype (Figure S5D)

if (!file.exists("axon_count.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/axon_count.qs?download=1",
        "axon_count.qs"
    )
}

axon_count <- qread("axon_count.qs")
plotBoxplotCustom(data = axon_count, y_var = "axon_count")

Boxplots of axon diameter per PNP subtype (Figure S5E)

g_ratio_grouped <-
    g_ratio |>
    group_by(sample) |>
    mutate(
        g_ratio = mean(g_ratio),
        ncv_tibial_motoric = mean(ncv_tibial_motoric),
        axon_diameter = mean(axon_diameter)
    ) |>
    ungroup() |>
    select(sample, level2, g_ratio, ncv_tibial_motoric, axon_diameter) |>
    distinct()

plotBoxplotCustom(g_ratio_grouped, y_var = "axon_diameter")

Correlation plots of NCV and histological measures (Figure S5F)

plotCor <- function(data, x, y) {
    ggplot(data, aes(x = .data[[x]], y = .data[[y]])) +
        geom_point() +
        geom_smooth(method = "lm") +
        theme_bw() +
        xlab(x) +
        ylab(y)
}

plotCor(g_ratio_grouped, x = "ncv_tibial_motoric", y = "g_ratio")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 12 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 12 rows containing missing values or values outside the scale range
(`geom_point()`).

plotCor(axon_count, x = "ncv_tibial_motoric", y = "axon_count")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 12 rows containing non-finite outside the scale range
(`stat_smooth()`).
Removed 12 rows containing missing values or values outside the scale range
(`geom_point()`).

plotCor(g_ratio_grouped, x = "ncv_tibial_motoric", y = "axon_diameter")

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 12 rows containing non-finite outside the scale range
(`stat_smooth()`).
Removed 12 rows containing missing values or values outside the scale range
(`geom_point()`).

Correlation plots of axon count and SC abundance (Figure S5G)

if (!file.exists("abundance_axon.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/abundance_axon.qs?download=1",
        "abundance_axon.qs"
    )
}
abundance_axon <- qread("abundance_axon.qs")

abundance_axon |>
    dplyr::filter(cell == "mySC") |>
    plotCor(x = "log_axon_normal", y = "count") +
    ylab("mySC (%)")

`geom_smooth()` using formula = 'y ~ x'

abundance_axon |>
    dplyr::filter(cell == "repairSC") |>
    plotCor(x = "log_axon_normal", y = "count") +
    ylab("repairSC (%)")

`geom_smooth()` using formula = 'y ~ x'

Quantification of predicted cluster abundance in Xenium (Figure S5I+M)

if (!file.exists("xenium_sc_t_nk.qs")) {
    download.file(
        "https://zenodo.org/records/14551660/files/xenium_sc_t_nk.qs?download=1",
        "xenium_sc_t_nk.qs"
    )
}
xenium_sc_t_nk <- qread("xenium_sc_t_nk.qs")
xenium_sc_t_nk |>
    ggplot(aes(x = condition, y = percent, fill = condition)) +
    geom_boxplot() +
    geom_point() +
    theme_classic() +
    facet_wrap(vars(cluster), scales = "free_y", nrow = 1) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.3)) +
    xlab("") +
    ylab("percentage") +
    scale_fill_manual(values = umap_figure@misc$level2_cols) +
    theme(legend.position = "none")

Manual CD3E quantification in Xenium (Figure S5J)

purrr::walk2(
    c("endoCD3E_density_sum", "epiCD3E_density"),
    c("CD3E density endoneurial", "CD3E density epineurial"),
    plotQuanti
)

Manual MS4A1 quantification in Xenium (Figure S5K)

purrr::walk2(
    c("endoMS4A1_density_sum", "epiMS4A1_density"),
    c("MS4A1 density endoneurial", "MS4A1 density epineurial"),
    plotQuanti
)

Session info

session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.3.1 (2023-06-16)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Europe/Berlin
 date     2025-03-29
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package              * version    date (UTC) lib source
 abind                  1.4-5      2016-07-21 [1] RSPM (R 4.3.0)
 Augur                  1.0.3      2024-04-05 [1] Github (neurorestore/Augur@fa5d837)
 backports              1.4.1      2021-12-13 [1] RSPM (R 4.3.0)
 beachmat               2.18.0     2023-10-24 [1] Bioconductor
 beeswarm               0.4.0      2021-06-01 [1] RSPM (R 4.3.0)
 Biobase                2.62.0     2023-10-24 [1] Bioconductor
 BiocGenerics           0.48.1     2023-11-01 [1] Bioconductor
 BiocNeighbors          1.20.2     2024-01-07 [1] Bioconductor 3.18 (R 4.3.1)
 BiocParallel           1.36.0     2023-10-24 [1] Bioconductor
 BiocSingular           1.18.0     2023-10-24 [1] Bioconductor
 bitops                 1.0-7      2021-04-24 [1] RSPM (R 4.3.0)
 BPCells              * 0.1.0      2024-01-14 [1] Github (bnprks/BPCells@654799d)
 broom                  1.0.5      2023-06-09 [1] RSPM (R 4.3.0)
 car                    3.1-2      2023-03-30 [1] RSPM (R 4.3.0)
 carData                3.0-5      2022-01-06 [1] RSPM (R 4.3.0)
 class                  7.3-22     2023-05-03 [1] RSPM (R 4.3.0)
 cli                    3.6.2      2023-12-11 [1] RSPM (R 4.3.0)
 cluster                2.1.6      2023-12-01 [1] RSPM (R 4.3.0)
 codetools              0.2-19     2023-02-01 [1] CRAN (R 4.2.2)
 colorspace             2.1-0      2023-01-23 [1] RSPM (R 4.3.0)
 cowplot                1.1.2      2023-12-15 [1] RSPM (R 4.3.0)
 crayon                 1.5.2      2022-09-29 [1] RSPM (R 4.3.0)
 data.table             1.14.10    2023-12-08 [1] RSPM (R 4.3.0)
 DelayedArray           0.28.0     2023-10-24 [1] Bioconductor
 DelayedMatrixStats     1.24.0     2023-10-24 [1] Bioconductor
 deldir                 2.0-2      2023-11-23 [1] RSPM (R 4.3.0)
 dichromat              2.0-0.1    2022-05-02 [1] RSPM (R 4.3.0)
 digest                 0.6.34     2024-01-11 [1] RSPM (R 4.3.0)
 dotCall64              1.1-1      2023-11-28 [1] RSPM (R 4.3.0)
 dplyr                * 1.1.4      2023-11-17 [1] RSPM (R 4.3.0)
 edgeR                  4.0.7      2024-01-12 [1] Bioconductor 3.18 (R 4.3.1)
 ellipsis               0.3.2      2021-04-29 [1] RSPM (R 4.3.0)
 EnhancedVolcano      * 1.20.0     2023-10-24 [1] Bioconductor
 evaluate               0.23       2023-11-01 [1] RSPM (R 4.3.0)
 fansi                  1.0.6      2023-12-08 [1] RSPM (R 4.3.0)
 farver                 2.1.1      2022-07-06 [1] RSPM (R 4.3.0)
 fastDummies            1.7.3      2023-07-06 [1] RSPM (R 4.3.0)
 fastmap                1.1.1      2023-02-24 [1] RSPM (R 4.3.0)
 fitdistrplus           1.1-11     2023-04-25 [1] RSPM (R 4.3.0)
 forcats              * 1.0.0      2023-01-29 [1] RSPM (R 4.3.0)
 furrr                  0.3.1      2022-08-15 [1] RSPM (R 4.3.0)
 future                 1.33.1     2023-12-22 [1] RSPM (R 4.3.0)
 future.apply           1.11.1     2023-12-21 [1] RSPM (R 4.3.0)
 generics               0.1.3      2022-07-05 [1] RSPM (R 4.3.0)
 GenomeInfoDb           1.38.5     2023-12-28 [1] Bioconductor 3.18 (R 4.3.1)
 GenomeInfoDbData       1.2.11     2023-11-13 [1] Bioconductor
 GenomicRanges          1.54.1     2023-10-29 [1] Bioconductor
 ggbeeswarm             0.7.2      2023-04-29 [1] RSPM (R 4.3.0)
 ggforce                0.4.1      2022-10-04 [1] RSPM (R 4.3.0)
 ggplot2              * 3.5.1      2024-04-23 [1] RSPM (R 4.3.0)
 ggpubr                 0.6.0      2023-02-10 [1] RSPM (R 4.3.0)
 ggraph                 2.1.0      2022-10-09 [1] RSPM (R 4.3.0)
 ggrepel              * 0.9.5      2024-01-10 [1] RSPM (R 4.3.0)
 ggridges               0.5.5      2023-12-15 [1] RSPM (R 4.3.0)
 ggsignif               0.6.4      2022-10-13 [1] RSPM (R 4.3.0)
 globals                0.16.2     2022-11-21 [1] RSPM (R 4.3.0)
 glue                   1.7.0      2024-01-09 [1] RSPM (R 4.3.0)
 goftest                1.2-3      2021-10-07 [1] RSPM (R 4.3.0)
 gower                  1.0.1      2022-12-22 [1] RSPM (R 4.3.0)
 graphlayouts           1.0.2      2023-11-03 [1] RSPM (R 4.3.0)
 gridExtra              2.3        2017-09-09 [1] RSPM (R 4.3.0)
 gtable                 0.3.4      2023-08-21 [1] RSPM (R 4.3.0)
 gtools                 3.9.5      2023-11-20 [1] RSPM (R 4.3.0)
 hardhat                1.3.0      2023-03-30 [1] RSPM (R 4.3.0)
 hms                    1.1.3      2023-03-21 [1] RSPM (R 4.3.0)
 htmltools              0.5.7      2023-11-03 [1] RSPM (R 4.3.0)
 htmlwidgets            1.6.4      2023-12-06 [1] RSPM (R 4.3.0)
 httpuv                 1.6.13     2023-12-06 [1] RSPM (R 4.3.0)
 httr                   1.4.7      2023-08-15 [1] RSPM (R 4.3.0)
 ica                    1.0-3      2022-07-08 [1] RSPM (R 4.3.0)
 igraph                 1.6.0      2023-12-11 [1] RSPM (R 4.3.0)
 ipred                  0.9-14     2023-03-09 [1] RSPM (R 4.3.0)
 IRanges                2.36.0     2023-10-24 [1] Bioconductor
 irlba                  2.3.5.1    2022-10-03 [1] RSPM (R 4.3.0)
 jsonlite               1.8.8      2023-12-04 [1] RSPM (R 4.3.0)
 KernSmooth             2.23-22    2023-07-10 [1] RSPM (R 4.3.0)
 knitr                  1.45       2023-10-30 [1] RSPM (R 4.3.0)
 labeling               0.4.3      2023-08-29 [1] RSPM (R 4.3.0)
 later                  1.3.2      2023-12-06 [1] RSPM (R 4.3.0)
 lattice                0.22-5     2023-10-24 [1] RSPM (R 4.3.0)
 lava                   1.7.3      2023-11-04 [1] RSPM (R 4.3.0)
 lazyeval               0.2.2      2019-03-15 [1] RSPM (R 4.3.0)
 leiden                 0.4.3.1    2023-11-17 [1] RSPM (R 4.3.0)
 lifecycle              1.0.4      2023-11-07 [1] RSPM (R 4.3.0)
 limma                  3.58.1     2023-10-31 [1] Bioconductor
 listenv                0.9.0      2022-12-16 [1] RSPM (R 4.3.0)
 lmtest                 0.9-40     2022-03-21 [1] RSPM (R 4.3.0)
 locfit                 1.5-9.8    2023-06-11 [1] RSPM (R 4.3.0)
 lubridate            * 1.9.3      2023-09-27 [1] RSPM (R 4.3.0)
 magrittr               2.0.3      2022-03-30 [1] RSPM (R 4.3.0)
 mapproj                1.2.11     2023-01-12 [1] RSPM (R 4.3.0)
 maps                   3.4.2      2023-12-15 [1] RSPM (R 4.3.0)
 MASS                   7.3-60     2023-05-04 [1] RSPM (R 4.3.0)
 Matrix                 1.6-5      2024-01-11 [1] RSPM (R 4.3.0)
 MatrixGenerics         1.15.0     2024-04-05 [1] Github (Bioconductor/MatrixGenerics@ed066a2)
 matrixStats            1.2.0      2023-12-11 [1] RSPM (R 4.3.0)
 mgcv                   1.9-1      2023-12-21 [1] RSPM (R 4.3.0)
 miloDE               * 0.0.0.9000 2024-04-05 [1] Github (MarioniLab/milODE@3f825b9)
 miloR                  1.10.0     2023-10-24 [1] Bioconductor
 mime                   0.12       2021-09-28 [1] RSPM (R 4.3.0)
 miniUI                 0.1.1.1    2018-05-18 [1] RSPM (R 4.3.0)
 munsell                0.5.0      2018-06-12 [1] RSPM (R 4.3.0)
 nlme                   3.1-164    2023-11-27 [1] RSPM (R 4.3.0)
 nnet                   7.3-19     2023-05-03 [1] RSPM (R 4.3.0)
 pals                 * 1.8        2023-08-23 [1] RSPM (R 4.3.0)
 parallelly             1.36.0     2023-05-26 [1] RSPM (R 4.3.0)
 parsnip                1.1.1      2023-08-17 [1] RSPM (R 4.3.0)
 patchwork              1.2.0      2024-01-08 [1] RSPM (R 4.3.0)
 pbapply                1.7-2      2023-06-27 [1] RSPM (R 4.3.0)
 pbmcapply              1.5.1      2022-04-28 [1] RSPM (R 4.3.0)
 pillar                 1.9.0      2023-03-22 [1] RSPM (R 4.3.0)
 pkgconfig              2.0.3      2019-09-22 [1] RSPM (R 4.3.0)
 plotly                 4.10.3     2023-10-21 [1] RSPM (R 4.3.0)
 plyr                   1.8.9      2023-10-02 [1] RSPM (R 4.3.0)
 png                    0.1-8      2022-11-29 [1] RSPM (R 4.3.0)
 Polychrome             1.5.1      2022-05-03 [1] RSPM (R 4.3.0)
 polyclip               1.10-6     2023-09-27 [1] RSPM (R 4.3.0)
 prodlim                2023.08.28 2023-08-28 [1] RSPM (R 4.3.0)
 progressr              0.14.0     2023-08-10 [1] RSPM (R 4.3.0)
 promises               1.2.1      2023-08-10 [1] RSPM (R 4.3.0)
 purrr                * 1.0.2      2023-08-10 [1] RSPM (R 4.3.0)
 qs                   * 0.25.7     2023-12-06 [1] RSPM (R 4.3.0)
 R6                     2.5.1      2021-08-19 [1] RSPM (R 4.3.0)
 randomForest           4.7-1.1    2022-05-23 [1] RSPM (R 4.3.0)
 RANN                   2.6.1      2019-01-08 [1] RSPM (R 4.3.0)
 RApiSerialize          0.1.2      2022-08-25 [1] RSPM (R 4.3.0)
 RColorBrewer           1.1-3      2022-04-03 [1] RSPM (R 4.3.0)
 Rcpp                   1.0.12     2024-01-09 [1] RSPM (R 4.3.0)
 RcppAnnoy              0.0.21     2023-07-02 [1] RSPM (R 4.3.0)
 RcppGreedySetCover     0.1.0      2018-01-24 [1] RSPM (R 4.3.0)
 RcppHNSW               0.5.0      2023-09-19 [1] RSPM (R 4.3.0)
 RcppParallel           5.1.7      2023-02-27 [1] RSPM (R 4.3.0)
 RCurl                  1.98-1.14  2024-01-09 [1] RSPM (R 4.3.0)
 readr                * 2.1.5      2024-01-10 [1] RSPM (R 4.3.0)
 recipes                1.0.9      2023-12-13 [1] RSPM (R 4.3.0)
 reshape2               1.4.4      2020-04-09 [1] RSPM (R 4.3.0)
 reticulate             1.34.0     2023-10-12 [1] RSPM (R 4.3.0)
 rlang                  1.1.3      2024-01-10 [1] RSPM (R 4.3.0)
 rmarkdown              2.25       2023-09-18 [1] RSPM (R 4.3.0)
 ROCR                   1.0-11     2020-05-02 [1] RSPM (R 4.3.0)
 rpart                  4.1.23     2023-12-05 [1] RSPM (R 4.3.0)
 rsample                1.2.0      2023-08-23 [1] RSPM (R 4.3.0)
 RSpectra               0.16-1     2022-04-24 [1] RSPM (R 4.3.0)
 rstatix                0.7.2      2023-02-01 [1] RSPM (R 4.3.0)
 rsvd                   1.0.5      2021-04-16 [1] RSPM (R 4.3.0)
 Rtsne                  0.17       2023-12-07 [1] RSPM (R 4.3.0)
 S4Arrays               1.2.0      2023-10-24 [1] Bioconductor
 S4Vectors              0.40.2     2023-11-23 [1] Bioconductor 3.18 (R 4.3.1)
 ScaledMatrix           1.10.0     2023-10-24 [1] Bioconductor
 scales                 1.3.0      2023-11-28 [1] RSPM (R 4.3.0)
 scattermore            1.2        2023-06-12 [1] RSPM (R 4.3.0)
 scatterplot3d          0.3-44     2023-05-05 [1] RSPM (R 4.3.0)
 scMisc               * 0.3.0      2024-04-01 [1] Github (mihem/scMisc@6b1e979)
 sctransform            0.4.1      2023-10-19 [1] RSPM (R 4.3.0)
 scuttle                1.12.0     2023-10-24 [1] Bioconductor
 sessioninfo          * 1.2.2      2021-12-06 [1] RSPM (R 4.3.0)
 Seurat               * 5.0.1.9004 2025-03-28 [1] Github (satijalab/seurat@9ad1627)
 SeuratObject         * 5.0.1      2023-11-17 [1] CRAN (R 4.3.1)
 shiny                  1.8.0      2023-11-17 [1] RSPM (R 4.3.0)
 SingleCellExperiment   1.24.0     2023-10-24 [1] Bioconductor
 sp                   * 2.1-2      2023-11-26 [1] RSPM (R 4.3.0)
 spam                   2.10-0     2023-10-23 [1] RSPM (R 4.3.0)
 SparseArray            1.2.3      2023-12-25 [1] Bioconductor 3.18 (R 4.3.1)
 sparseMatrixStats      1.13.4     2024-04-05 [1] Github (const-ae/sparseMatrixStats@4c6e814)
 spatstat.data          3.0-3      2023-10-24 [1] RSPM (R 4.3.0)
 spatstat.explore       3.2-5      2023-10-22 [1] RSPM (R 4.3.0)
 spatstat.geom          3.2-7      2023-10-20 [1] RSPM (R 4.3.0)
 spatstat.random        3.2-2      2023-11-29 [1] RSPM (R 4.3.0)
 spatstat.sparse        3.0-3      2023-10-24 [1] RSPM (R 4.3.0)
 spatstat.utils         3.0-4      2023-10-24 [1] RSPM (R 4.3.0)
 statmod                1.5.0      2023-01-06 [1] RSPM (R 4.3.0)
 stringfish             0.16.0     2025-03-29 [1] Github (traversc/stringfish@b943b1c)
 stringi                1.8.3      2023-12-11 [1] RSPM (R 4.3.0)
 stringr              * 1.5.1      2023-11-14 [1] RSPM (R 4.3.0)
 SummarizedExperiment   1.32.0     2023-10-24 [1] Bioconductor
 survival               3.5-7      2023-08-14 [1] RSPM (R 4.3.0)
 tensor                 1.5        2012-05-05 [1] RSPM (R 4.3.0)
 tester                 0.1.7      2013-11-14 [1] RSPM (R 4.3.0)
 tibble               * 3.2.1      2023-03-20 [1] RSPM (R 4.3.0)
 tidygraph              1.3.0      2023-12-18 [1] RSPM (R 4.3.0)
 tidyr                * 1.3.0      2023-01-24 [1] RSPM (R 4.3.0)
 tidyselect             1.2.0      2022-10-10 [1] RSPM (R 4.3.0)
 tidyverse            * 2.0.0      2023-02-22 [1] RSPM (R 4.3.0)
 timechange             0.2.0      2023-01-11 [1] RSPM (R 4.3.0)
 timeDate               4032.109   2023-12-14 [1] RSPM (R 4.3.0)
 tweenr                 2.0.2      2022-09-06 [1] RSPM (R 4.3.0)
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 xtable                 1.8-4      2019-04-21 [1] RSPM (R 4.3.0)
 XVector                0.42.0     2023-10-24 [1] Bioconductor
 yaml                   2.3.8      2023-12-11 [1] RSPM (R 4.3.0)
 yardstick              1.2.0      2023-04-21 [1] RSPM (R 4.3.0)
 zlibbioc               1.48.0     2023-10-24 [1] Bioconductor
 zoo                    1.8-12     2023-04-13 [1] RSPM (R 4.3.0)

 [1] /home/mischko/R/x86_64-pc-linux-gnu-library/4.3
 [2] /opt/R/4.3.1/lib/R/library

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